Identification of key genes associated with esophageal adenocarcinoma based on bioinformatics analysis

Abstract

Background: Esophageal adenocarcinoma (EAC) is an aggressive malignancy and accounts for the majority of cancer-related death worldwide. It is often diagnosed at an advanced stage and entails a poor prognosis for those afflicted. The mechanisms of its pathogenesis and progress remain unclear and require urgent elucidation. This study aimed to identify specific genes and potential pathways associated with the progression and prognosis of EAC using bioinformatics analyses. Methods: EAC microarray datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were analyzed to identify differentially expressed genes (DEGs) using bioinformatics analysis. The DEGs in TCGA were then analyzed to construct a co-expression network by weighted correlation network analysis (WGCNA), and module-clinical trait relationships were analyzed to explore the genes that associated with clinicopathological parameters of EAC. Gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyses were performed for the cancer-related genes, and a DEG-based protein-protein interaction (PPI) network was used to extract hub genes through Cytoscape plugins. The consensus survival analysis for EAC (OSeac) was performed to identify the prognosis-related genes. The immune infiltration was evaluated by tumor immune estimation resource (TIMER) algorithms, and a risk score prognostic model was established using univariate, multivariate Cox proportional hazards regression, and lasso regression analysis. Results: Ultimately, 190 cancer-related DEGs were identified, 6 of which were found to play vital roles in the progression of EAC, including ACTA2, BGN, CALD1, COL1A1, COL4A1, and DCN. The risk score prognostic model consisted of 6 other genes that had an important impact on the prognosis of EAC, including CLDN3, EPB41L4A, ESM1, MT1X, PAQR5, and PLAU. The area under the curve of the prognostic model for predicting the survival of patients at 1, 2, and 3 years was 0.707, 0.702, and 0.726, respectively. Conclusions: This study identified several genes with the potential to become useful targets for the diagnosis and treatment of EAC. The 6-gene-related risk score prognostic model and nomogram based on these genes may be a reliable tool for predicting the prognosis of patients with EAC.